RISK BASED METHODOLOGY FOR ASSESSING AVOIDED DEFORESTATION WITH APPLICATION IN ICF FOREST PROGRAMMES IN SOUTHERN GHANA FINAL REPORT, June 2015 Authors: Veronique Morel, Ecometrica Karin Viergever, Ecometrica Richard Tipper, Ecometrica Edward Mitchard, The University of Edinburgh Acknowledgements: Ecometrica and The University of Edinburgh would like to acknowledge the input from the following experts, obtained during video conference meetings in February and March 2015, in identifying the main risk parameters: Winston Asante (Kwame Nkrumah University of Science and Technology), Bright Obeng Kankam (CSIR-FORIG), Edward Obiaw (GFC-RMSC), Julia Falconer (DFID). Neil Scotland (DFID) also assisted in our efforts to gather information. Ecometrica acquired all data sources and did the risk mapping analyses based on our interpretation of the expert input. The University of Edinburgh was the main point of contact with in-country experts. This project was funded by the European Space Agency (ESRIN Contract No.4000112345/14/I-NB: Earth Observation Support for Assessing the Performance of UK government’s ICF Forest Projects), with additional support from NERC (Innovation Voucher Scheme).
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RISK BASED METHODOLOGY FOR ASSESSING
AVOIDED DEFORESTATION WITH APPLICATION IN
ICF FOREST PROGRAMMES IN SOUTHERN GHANA
FINAL REPORT, June 2015
Authors:
Veronique Morel, Ecometrica
Karin Viergever, Ecometrica
Richard Tipper, Ecometrica
Edward Mitchard, The University of Edinburgh
Acknowledgements:
Ecometrica and The University of Edinburgh would like to acknowledge the input from the following
experts, obtained during video conference meetings in February and March 2015, in identifying the
main risk parameters: Winston Asante (Kwame Nkrumah University of Science and Technology), Bright
Obeng Kankam (CSIR-FORIG), Edward Obiaw (GFC-RMSC), Julia Falconer (DFID). Neil Scotland (DFID) also
assisted in our efforts to gather information.
Ecometrica acquired all data sources and did the risk mapping analyses based on our interpretation of
the expert input. The University of Edinburgh was the main point of contact with in-country experts.
This project was funded by the European Space Agency (ESRIN Contract No.4000112345/14/I-NB: Earth
Observation Support for Assessing the Performance of UK government’s ICF Forest Projects), with additional
support from NERC (Innovation Voucher Scheme).
Risk Based Methodology for Assessing Avoided Deforestation with application in ICF Forest Programmes in southern Ghana
2 Description of study area ...................................................................................................................................... 4
4.1 Accessibility: Risk of deforestation associated with access by road ............................................................ 6
4.2 Accessibility/Cultivability: Risk due to Proximity to Previous Sites of Deforestation .................................. 8
4.3 Accessibility: Proximity to cities and towns ................................................................................................. 9
4.4 Protected Areas .......................................................................................................................................... 11
5 Methods and calculations ................................................................................................................................... 11
6 Note on further work .......................................................................................................................................... 14
Risk Based Methodology for Assessing Avoided Deforestation with application in ICF Forest Programmes in southern Ghana
Ecometrica | University of Edinburgh
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1 INTRODUCTION
The International Climate Fund (ICF) was set up by the UK government in 2011 with the aim of working
in partnership with developing countries to reduce carbon emissions through promoting low carbon
development, to help the world’s poorest people adapt to climate change and reduce deforestation.
Forest activities funded under ICF should support developing country actions on Reducing Emissions
from Deforestation and forest Degradation (REDD) and contribute to low carbon growth that reduces
poverty. An improved understanding of forest areas at risk as well as historic and ongoing deforestation
likely to occur in the absence of conservation interventions is important in deciding how to target
interventions and how to evaluate the impact of conservation measures in terms of avoided
deforestation.
The following risk-based method takes advantage of earth observation data and geospatial information
products and has been devised to apply to large scale programmes in areas where broadly similar
processes, legal and institutional constraints, play out across forest ecosystems.
The output of the method is an estimate of avoided deforestation derived from the amount of expected
forest loss within an area over a 20 year period versus observed annual forest loss. Expected loss is
estimated by applying an ACEU - type1 risk model which assumes that land areas are at greater risk of
deforestation and degradation if they are easily accessible, are suitable for cultivation, have an
extractable value, and are unprotected. The methodology does not provide a prediction of future forest
loss but assigns relative risk values, based on the ACEU criteria.
Each of the four ACEU parameters are defined and assigned a level of risk based on assessments of
region-specific drivers of forest loss and land use change. The resulting risk map is intended to aid
project developers and conservation organisations wishing to target efforts to areas where they are
most needed.
The method was assessed for feasibility in 3 ICF project areas with markedly different forest types and
drivers of land use change in southern Ghana, Terai and Churia in Nepal and Brazilian cerrado. This
document describes the methodology used to produce a risk of deforestation map in each of these
areas.
An important part of this work was in defining forest extents in each region, identifying drivers of forest
loss in consultation with local experts and assessing the quality and availability of data. It is suggested
1 The ACEU risk model determines an overall level of risk as the product of the risks associated with each of the four ACEU
parameters: A = Accessible – local actors able to reach the area (RA); C = has Cultivable value – land can be used for subsistence or commercial crops (RC) E = Extractable Value – forest biomass has economic value (RE), U = Un/Protection Status – land tenure regime does not prevent extraction or conversion (RU). Risk is calculated as: RISK FACTOR = (RU)* (RC)*( RE)* (RA)
Risk Based Methodology for Assessing Avoided Deforestation with application in ICF Forest Programmes in southern Ghana
Ecometrica | University of Edinburgh
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that the risk maps may be updated in the future, to take account of new risks and changes to the
understanding of drivers.
2 DESCRIPTION OF STUDY AREA
An area of 14 132 457 ha covering the southern Ghana including the Brong Ahafo, Volta, Ashanti,
Greater Accra, Central, Eastern and Western regions was chosen for this risk analysis. This area was
chosen because the majority of high biomass forest is found in the south of Ghana, located in blocks of
forest mostly under some form of protection. The north of the country is too dry to support tall forest,
and almost no trees felled there have international commercial value, so this is not the focus of the ICF
activities in the country. ICF activities are mostly at a general policy level in Ghana, and thus no
particular area was identified to be a focus beyond including blocks of tall forest.
Figure 1: Southern regions of Ghana included in the risk analysis.
Risk Based Methodology for Assessing Avoided Deforestation with application in ICF Forest Programmes in southern Ghana
Ecometrica | University of Edinburgh
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3 DEFINING FOREST EXTENTS
The first step for mapping risk of deforestation, is to identify the forest extent within the study area.
Ghana’s official definition of “forest” is woodland with a minimum canopy cover of 15% and minimum
area of 0.1 ha. Forest extent within the study area was obtained applying the above definition using
Global tree canopy cover for the year 2000, produced by Hansen et al (2013)2 , updated to 2013 using
Global Forest Cover Loss (2000-2012) data 3 (shown in Figure 2).
Figure 2: Forest extent derived from
Hansen et al (2013) Global Tree Canopy
Cover for 2000 dataset and Global Forest
Cover Loss up to 2012 dataset. Areas
over 0.1 hectare and with more than 15%
canopy cover were classed as forest.
2 Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R.
Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, & J. R. G. Townshend (2013) High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53 http://earthenginepartners.appspot.com/science-2013-global-forest.
3 Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R.
Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, & J. R. G. Townshend (2013) High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53 http://earthenginepartners.appspot.com/science-2013-global-forest.